Following the previous methods [1]}, [2]}, we train the models on the train splits of four datasets GOT10k [3]}, TrackingNet [4]}, LaSOT [5]}, and COCO [6]} and report the success score (SUC) for the TrackingNet dataset and LaSOT dataset, and the average overlap (AO) for GOT10k.
We use the SwinTrack [1]} to train and evaluate our pre-trained models with the same data augmentations, training, and inference settings.
We sample 131072 pairs per epoch and train the models for 300 epochs. We use the AdamW optimizer with a learning rate of \(5e\) -4 for the head, a learning rate of \(5e\) -5 for the backbone, and a weight decay of \(1e\) -4. We decrease the learning rate by a ratio of \(0.1\)  at the 210th epoch. We set the sizes of search images and templates as \(224\times 224\)  and \(112\times 112\) .
<TABLE>